Design and build complex ETL/ELT pipelines for structured and unstructured data sources using GCP native services like Cloud Storage, Dataflow, and Dataproc, Data Integration & ETL/ELT
Build and orchestrate automated data pipelines using Fivetran, Google DTS, and custom ETL/ELT frameworks
Create robust ingestion patterns for batch and streaming data
Troubleshoot data integration issues, latency problems, and pipeline failures
Partner with application teams to integrate SaaS, on-prem, and cloud sources
Requirements
Bachelor’s degree in computer science, Information Systems, or related field.
Tackle new and challenging problems by building a new generation of highly scaled data processing and analytics systems.
Compensation
The compensation offered for this position will depend on qualifications, experience, and work location. For candidates who receive an offer at the posted level, the starting base salary (for non-sales roles) or base salary + commission target (for sales/commissioned roles) is expected to be between $147000 - $237500/YR. The offered compensation may also include restricted stock units and a bonus.
Responsibilities
Build highly scaled data processing and analytics systems
Contribute to architecture, design and development of features
Solve complex problems in pipeline scaling and data storage to facilitate dashboards
Suggest and implement improvements to the development processes
Work with DevOps and Technical Support teams to investigate and resolve critical customer defects
Build and scale data processing and analytics systems for Palo Alto Networks’ Strata Logging Service, contributing to architecture, design, and development of features while collaborating with DevOps and Technical Support teams.
Compensation
$147,000.00 - $237,500.00/yr
Responsibilities
Design, develop, and scale enterprise data processing and analytics systems
Architect and implement features for the Strata Logging Service
Solve complex problems in pipeline scaling and data storage
Improve development processes
Investigate and resolve critical customer defects with DevOps and Technical Support
Lead a high-performing team of Data Engineers and Web Analytics Engineers to design and optimize scalable data warehouses and pipelines on Google Cloud Platform.
Responsibilities
Lead, mentor, and code‑review a team of Data Engineers and Web Analytics Engineers
Oversee end‑to‑end design, development, and optimization of scalable data warehouses and pipelines (ETL/ELT)
Act as the hands‑on authority for orchestrating data workflows via Google Cloud Infrastructure especially BigQuery & Dataflow
Own the integrity and scalability of distributed data processing systems within GCP
Architect technical translation of high‑level business requirements into scalable AI and data solutions
Requirements
Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related quantitative field
Nice to have: Google Cloud Platform (GCP) or similar large‑scale distributed data infrastructure
Nice to have: AI toolkits such as ADK, Anti Gravity, Vertex AI Agent Engine
Nice to have: Data warehouses tailored for business performance management
We're looking for a EIM Architect who will be fully dedicated to our Enterprise Data Platform (EDP) — a modern, GCP-native platform that supports both the migration of workloads from our legacy on-premises EDW and the build-out of net-new enterprise data products across marketing, customer, supply chain, and other retail business domains.
A Staff Data Engineer role focused on leading design and architecture of scalable data platforms on GCP, managing engineering teams, and driving data strategy roadmaps.
Locations (2)
Bangalore, Karnataka, India
Chennai, Tamil Nadu, India
Responsibilities
Lead the design and architecture of scalable, resilient, and high-performance data platforms on Google Cloud (GCP).
Define and implement data engineering best practices, including data modeling, pipeline orchestration, and data governance.
Architect solutions using Big Data technologies such as Apache Spark, Hadoop ecosystem, and distributed systems.
Ensure alignment to enterprise data strategy, including data products, SSoT, and platform standardization.
Drive end-to-end data pipeline development using Scala, Spark, and modern ETL/ELT frameworks.
Senior Data Engineer responsible for designing, building, and maintaining scalable data platforms on Google Cloud.
Responsibilities
Implement the design and architecture of scalable, resilient, and high-performance data platforms on GCP
Implement data engineering best practices, including data modeling, pipeline orchestration, and data governance
Develop solutions using Big Data technologies such as Apache Spark, Hadoop ecosystem, and distributed systems
Drive end-to-end data pipeline development using Scala, Spark, and modern ETL/ELT frameworks
Lead development of batch and real-time data processing pipelines leveraging GCP services like BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage
Lead design, architecture, and delivery of scalable data platforms on Google Cloud, manage a team of data engineers, and drive data strategy and modernization initiatives.
Locations (2)
Bangalore, India
Chennai, Tamil Nadu, India
Responsibilities
Lead the design and architecture of scalable, resilient, and high-performance data platforms on GCP
Define and implement data engineering best practices, including data modeling, pipeline orchestration, and data governance
Architect solutions using Big Data technologies such as Apache Spark, Hadoop ecosystem, and distributed systems
Drive end-to-end data pipeline development using Scala, Spark, and modern ETL/ELT frameworks
Lead development of batch and real-time data processing pipelines leveraging GCP services
L.L.Bean uses a market-based compensation approach. The anticipated starting base pay for this position is $120,200.00 - $150,200.00. Individual compensation may vary based on factors such as a candidate’s experience, qualifications, education, geographic location, skills, competencies, and proficiency for the role.
Responsibilities
Design end-to-end data solutions on EDP — spanning ingestion, transformation, and consumption — for both migration waves and net-new use cases, taking designs from conceptual through logical and physical models.
Define source-to-target mappings and transformation designs, including handling of data types, nulls, defaults, slowly changing dimensions, and business rule logic.
Make hands-on GCP service selection and design decisions across BigQuery, Dataflow, Cloud Composer, Dataform, and Cloud Storage — balancing performance, cost, security, and operability.
Design dimensional and analytics-ready data models (star schemas, fact/dimension structures) that align with downstream BI and consumption needs.
Apply security, access, and least-privilege design principles at the dataset and domain level in line with platform standards.
Requirements
Bachelor's degree in a relevant field or equivalent work experience.
Nice to have: Experience in retail, consumer, or omni-channel domains
Nice to have: Prior work on platform modernization or EDW-to-GCP migration